POLLING BOOTH DATA
Extracting insight from a full population base is often not easy - this sort of data comes irregularly and is often biased by unbalanced samples. The doubt thrown into the Australian census this year has added further to this dilemma. However another great 'survey' also occurred this year, that being the federal elections. This is an excellent resource for enhancing your data insight. Merging it with other data sources including your digital data not only gives increased insight into voting dynamics, but also allows you to pull new analytics to guide our own organisations. This may involve more targeted acquisition tactics whilst also understanding the political affiliations of your current contacts and supporters to engage with them in the best possible way.
Overall Division/Electorate results themselves have limited value - these are large areas and a lot of insight can be lost amongst the disparate and diverse populations. But when we move into smaller catchments - in this case the polling booth location - we can pull much richer insight. The data is gradually becoming available for the 2016 elections on the AEC website.
Below we can see a tool we have developed mapping the results of each polling location. Each dot represents a polling address, coloured by the top first preference party (of the top 10 parties nationally) in that location. By drilling into the map and hovering over each dot you can see the full results of that location.
Mapping 2016 Federal Election Results by Polling Booth
Now that we have this data we can start enhancing it with additional data. We do of course have to make assumptions, mainly that most people will vote at the nearest polling booth. This is how the insight is being generated. This will not always be the case - the closest location may not be the most convenient or, more importantly, have the best sausages! Nonetheless, given it is the weekend it is fair to assume this will be mostly the case and diversions will not significantly distort any insight.
Below we have the results overlaid with the SEIFA Index of Relative Socio-Economic Disadvantage (10 being the most wealthy, 1 being the most deprived). Immediately in the map below we can see a strong relationship between the polling place winner and the surrounding level of wealth.
Mapping 2016 Federal Election Results by Polling Booth with SEIFA Disadvantage Score
If we aggregate across all of Australia we can see the clear skew between political party and the SEIFA score (based on each household voting at the nearest polling booth). Below confirms the assumptions we have that Labour is favoured by the less well-off while the Liberals are more favoured by the wealthy. The Greens also skew to being a more wealthy party while the Nationals are skewed to lower wealth (this is driven a lot by the areas in which they are participating although the entire Coalition is still heavily skewed to the wealthy).
This relationship is even stronger if we look at local unemployment rates. Unemployment is perhaps a much more tangible circumstance thus driving a stronger relationship to voting behaviour.
Ancestry also skews voting behaviour, although not as strongly. Labour skews to more diverse populations whereas the Coalition skews to those with at least one Australian-born parent. Some of the minor parties skew exactly as one would expect!
This data is very powerful for political strategy and is being utilised by most parties. None of this insight will be new to them and much of their campaigning and media strategies will be tied to this. This will be overlaid with locational socio-economic data, along with individual data from sources such as social media and past interactions with canvassers.
See our Resources page for some of the vast array of micro-level analytical data available.
Below we can see the mapping of Green party supporters. They are clearly clustered in larger numbers in certain parts of the country, including Central Sydney and Newtown.
Mapping 2016 Federal Election Green Party Voters
The data can also be put to use for organisational interests. For example, an organisation may be looking to target wealthy Green voters in Melbourne aged between 35 and 64. By merging all this data they can be very targeted in their locational strategy and geographic marketing. Below it can be seen the best overall locations are north of the city around Fitzroy and Northcote, even though the hub of Green voters is in the CBD and the wealth is out west.
Mapping Targeted Wealthy Green Voters Aged 35 to 65 in Greater Melbourne
Election data does not answer all the questions but does provide another arrow to the quiver of your analytics arsenal. I have only broad brushed basic analysis and additional value will also lie in more sophisticated techniques. The data benefits from being very recent, comprehensive across all of Australia and it is freely accessible. With a little bit of creativity it can enhance your analytics significantly and fine tune your strategy and tactics that little bit more. That is something definitely worth voting for!